The present findings add to the database of genetic information on strain differences in ethanol preference and intake (e.g., Rodgers et al., 1966
; Belknap et al. 1993
). It should be noted that the design we employed offered relatively short (4 day) periods of access to each solution, since recent findings indicated that two-bottle preference tests (range of 1 – 6 days) were most sensitive to detect strain differences when they lasted 4 days (Tordoff and Bachmanov, 2002
). Additionally, we adopted this approach in order to compare the results with previous work more directly. However, we cannot exclude a potential role for temporal changes in preference in our results. That is, some strains may simply show patterns of increasing or decreasing preference for ethanol over many days, and the results might have been different for any particular concentration offered if we had tested for more days or without the prior history of solutions preceding it as in our design. With this in mind, ethanol consumption was significantly and positively correlated with previous work (e.g., Belknap et al., 1993
), as was ethanol preference ratio (e.g., Rodgers, 1966
). These robust correlations among studies conducted over a span of 40 years provide evidence for the stability of the ethanol preference drinking phenotype (discussed in more detail in Wahlsten et al., 2006
Estimates of genetic effect size for ethanol consumption and preference were comparable, suggesting that the continuous access, two-bottle choice procedure provides similar information regarding the contribution of genetic factors to measures of consumption and preference. It is notable that the present findings suggest that 44–60% of the variance in consumption/preference of unsweetened ethanol solutions and 58–64% of the variance in consumption/preference of sweetened ethanol solutions could be explained by genotype. These estimates are comparable to those in Belknap et al. (1993)
and provide strong support for the conclusion that all of the consumption/preference measures were highly and significantly genetically determined.
The finding that alcohol consumption/preference measures share substantial common genetic influence suggests that identifying specific genes influencing any one of these traits (through QTL mapping or gene expression profiling, for example) is likely to be informative for the other traits. The utility of this strategy is provided by recent work that employed a meta-analysis of microarray data from different genetic models of high and low alcohol consumption in their analysis of alcohol preference (Mulligan et al., 2006
). This analysis identified genes covering a range of cellular pathways, such as cellular homeostasis and neuronal function, as well as genes with uncharacterized functions. It is not surprising that genes related to neuronal adaptation and homeostasis would be important for alcohol-related responses following varying levels of consumption.
Strains ranked highest in terms of 10E dose consumed were the three C57/58 family strains (B6, C58 and C57BLKS). At the opposite end of the spectrum, the D2 (Little’s DBA + lineage), BUB (Swiss lineage) and BTBR strains consumed the lowest doses of ethanol. The BTBR strain derives from Castle’s strains, most of whose members are the various substrains of 129 strain mice that serve as the embryonic stem cell donor strains for most null mutant and transgenic over-expression lines. One consideration is that the current design, like previous experiments, does not allow us to identify the nature of the motivation of each strain to consume particular ethanol concentrations. One high-preferring strain may be seeking the taste of ethanol, while another seeks its pharmacological effect. Clearly, relative preference for saccharin also can influence the strain-specific responses to saccharin adulteration of ethanol solutions.
The addition of saccharin to the ethanol solutions increased ethanol intake in most genotypes, but it also changed the strain distribution pattern of ethanol intake. In general, strains that exhibited < 80% preference for 0.2S did not significantly increase their intake of the sweetened versus unsweetened ethanol solutions. The exceptions were the FVB and I/Ln strains, which significantly increased their intake of sweetened versus unsweetened ethanol solutions while preference for 0.2S was 78% and 71%, respectively. Interestingly, the addition of saccharin to the ethanol solutions increased the ethanol dose consumed in the SM, PERA, FVB and BALB strains to levels comparable to that in the C57/58 family strains. Based on the range in overall saccharin consumption (see for strain distribution) as well as preference for 0.2S in these strains (from 90% in SM to 78% in FVB; ), it is difficult to determine whether the increased intake of sweetened ethanol solutions was due to the strong relative avidity for saccharin (so that the strains would tolerate ethanol’s aversive effects more than other strains) or the reduced taste of ethanol when given in saccharin.
Another pattern of consumption was observed in strains with low consumption of unsweetened and sweetened ethanol (LP, A, BUB, BTBR and D2), which could be due to a combination of factors (discussed in Belknap et al., 1993
). Early work has documented that D2 mice were more sensitive to the aversive taste or odor of ethanol, when compared to B6 mice (Belknap et al., 1978
), but the specific role of odor or taste in determining strain-specific alcohol intake has not been thoroughly tested across multiple strains. Saccharin consumption in these five strains fell in the mid to low end of the strain distribution pattern (), while preference for 0.2S ranged from 78% (D2) to 55% (BTBR). Even though saccharin should have changed the taste of alcohol, the addition of saccharin did not produce a marked increase in ethanol consumption, suggesting that these five strains may have been reacting primarily to the odor of ethanol in maintaining their relative avoidance of the ethanol solutions. It also is possible that a higher concentration of saccharin might have masked the taste of the ethanol solutions more effectively in these strains. However, recent findings indicate that mice with a null mutation in one of three genes that are important for taste transduction (Gnat3
, or Trpm5
) exhibited reduced consumption of and preference for alcohol (6 – 12%) and saccharin (0.033 – 0.066%) solutions (Blednov et al., 2007
). Since polymorphisms in the Tas1r3
gene (believed to be identical to the mouse Sac
locus, important for saccharin preference) were strongly associated with saccharin preference in a panel of 30 inbred strains (Reed et al., 2004
), it is possible that polymorphisms in genes important for taste transduction contribute to the low alcohol consumption in select inbred strains. It should be noted that some strains may respond to adulteration of ethanol solutions with other compounds by increasing intake, as D2 mice have been shown to increase their intake of and two-bottle preference for ethanol when it was added to non-alcoholic beer (Grisel et al., 2007
). Finally, a recent study has shown that in rats, sucrose-responsive neurons in the nucleus of the solitary tract showed a much larger, concentration-dependent response to an oral ethanol stimulus than sucrose-unresponsive cells (Lemon et al., 2004
). This suggests a possible physiological basis for differential modulation of ethanol’s tase-related cue properties in saccharin preferring versus non-preferring genotypes.
Although B6 exhibited the highest consumption and preference for all ethanol solutions examined, recent work determined that preference for 10 – 30% ethanol solutions was significantly increased over that of B6 in an F1 hybrid cross of B6 and FVB mice (Blednov et al., 2005
). This finding appeared to be selective for 2-bottle preference procedures, as the B6 and F1 hybrids did not differ in ethanol consumption with the “drinking in the dark” procedure, where mice had 2 hr of access to a single ethanol bottle containing a 20% ethanol solution (Crabbe and Rhodes, unpublished). Nonetheless, additional 2-bottle preference studies determined that consumption of ethanol solutions (12 – 36%) also was significantly increased in F1 hybrid crosses of the FVB or SJL strains with B6 (Blednov, unpublished). These findings suggest that other F1 crosses with B6 may show overdominance with regard to consumption of ethanol in a 2-bottle choice procedure and suggest that a single gene does not control preference drinking.
With the exception of saccharin alone, consumption of the sweetened and unsweetened ethanol solutions was significantly influenced by sex. In general, ethanol intake was higher in female than in male mice, but not in all genotypes. An examination of sex differences across all three unsweetened ethanol solutions determined that there was a consistent and significant increase in ethanol intake in female mice from the PERA and SM strains. There was a significant sex difference in the consumption of the 3E and 6E solutions in the CZECH strain. Female B6 mice had significantly greater intake of 6E, with a trend for an increase in 10E, when compared to their male counterparts. Nonetheless, these findings are consistent with clinical and preclinical studies documenting the existence of sex differences in sensitivity to a number of alcohol-related behaviors associated with neuroadaptation and reinforcement (e.g., Devaud et al., 2003
; Finn et al., 2004a
, 2004b; Green et al., 1999
; Hashimoto and Wiren, 2007
; Lancaster, 1995
; Middaugh and Kelly, 1999
; Middaugh et al., 1999
; Rhodes et al., 2007
; Vivian et al., 2001
; Wiren et al. 2006
Genetic correlations, or the degree to which two phenotypes show common genetic influence, were conducted. In addition to the high positive correlations between preference and consumption in the present and earlier 2-bottle choice studies, continuous access 10E preference drinking was significantly, positively correlated with consumption of 20E over a 4 hr period in a single bottle test (Rhodes et al., 2007
). This finding suggests that genetic differences in propensity to consume alcohol may be comparable across various ethanol concentrations, access periods and choice conditions. Consistent with a recent meta-analysis that reported a genetic relationship between high ethanol withdrawal and low ethanol consumption (Metten et al., 1998
), ethanol intake in the current study tended to be negatively correlated with chronic ethanol withdrawal severity (Metten and Crabbe, 2005
). Ethanol intake also was negatively correlated with impaired performance on the rotarod following injection of an ataxic dose of ethanol. This finding may be specific to the rotarod, as strain correlational studies have shown that different assays of ethanol intoxication tend to be influenced by different genes (Crabbe et al., 2005
). It is of interest, however, as a substantial human literature has found a genetic relationship between low sensitivity to alcohol and eventual diagnosis of alcohol dependence in offspring with a positive family history (Schuckit, 1994
; Schuckit et al., 2004
Correlations between ethanol consumption and MPD variables also suggest the pleiotropic effect of genes related to preference for other solutions (sodium chloride, potassium chloride, calcium chloride), activity level and anxiety, as well as bone mineral density. Since the MPD variables reflect data in alcohol naive strains, the genetic relationship between alcohol consumption and measures of activity and anxiety may reflect the strain differences in locomotor activity and anxiety that exist (e.g., Finn et al., 2003
; Trullas and Skolnick, 1993
; Wahlsten et al., 2003
). With regard to bone mineral density, limited evidence suggests that there is no consistent relationship between high bone mass or strength with selection for high alcohol consumption and/or preference in rat lines (Alam et al., 2005
), whereas data from animal models of chronic or excessive alcohol intake suggest that chronic exposure to high alcohol doses can inhibit bone formation (e.g., Wahl et al., 2006
). All these correlational analyses must be viewed with the caveat that the significance values reported are uncorrected for multiple comparisons. Nonetheless, these correlations provide some suggestions for future examinations of genetic relationships between alcohol consumption/preference and other phenotypes.
In conclusion, the present findings add new strains to the existing strain mean database on genetic differences in consumption of, and preference for, sweetened and unsweetened ethanol solutions as well as saccharin. Although there was no strain identified that consumed higher doses of ethanol than the B6, estimates of genetic effect size indicated that up to 60% or 64% of the variance in consumption/preference of unsweetened or sweetened ethanol solutions, respectively, could be explained by genotype. The fact that the measures of ethanol preference and consumption were highly and significantly correlated genetically indicates that the growing strain mean databases will be an important resource for future studies aimed at the identification of genes important for high (or low) alcohol consumption.